Fraud Rate Research Articles

Research on Backers’ Motivations in Reward-Based Crowdfunding with Analytic Network Process

Crowdfunding is an innovative conceptualized alternative to traditional funding mechanisms which bypasses bureaucracy and eliminates geographical barriers by more contributor with less contribution. The motivation criteria that mobilize backers for crowdfunding especially for those who don’t have a certain financial expectation are an important research topic. In this study, the factors that affect the motivation of the backers, especially in reward-based crowdfunding campaigns were researched, prioritized and their effects on funding decision were examined by using Analytic Network Process (ANP). As a matter of fact, it is seen that the most important motivation factor is reliability of platform instead of reward expectation for those who participate in reward-based crowdfunding campaigns. Considering crowdfunding is still quite new, crowdfunding platforms are the most prominent part of this process and the promised rewards have generally low monetary value or the high fraud rate, this result isn’t considered a surprise.

A Systematic Review and Qualitative Assessment of Fraud Detection Methodologies in Health Care

Health care fraud is a costly, challenging problem in health insurance. This study provides a systematic evaluation and synthesis of the methodologies and data samples used in current peer-reviewed studies from different academic fields on characterizing health care fraud. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to guide reviewing the literature. In addition, a qualitative case study approach was employed to assess the studies included in the review in order to independently confirm the conclusions of the systematic review. Out of the 450 articles that were identified by the search criteria, 27 studies were deemed as relevant and included in the analysis. Using 24 variables designed from the literature to synthesize the fraud detection methodologies, the systematic review showed an inability to compare studies quantitatively because few studies reported the accuracy of their detection methods or the overall rate of fraud. The qualitative assessment independently confirmed that prior studies are highly diverse, with the only common characteristic being widespread use of data mining methods. Applying a previously validated approach that has not been taken by prior health care fraud reviews, our qualitative method showed high validity in terms of reviewers’ agreement on the classification of fraud detection methods (r = 93%). Two limitations of this study are that the strength of the evidence is reliant on the quality and number of studies previously performed on the topic, and our systematic review and qualitative results were limited to the text of the final studies as published in peer-reviewed journals. The main gaps we identified are the need to validate existing methods, lack of proof of intent to commit fraud, absence of a fraud rate estimate in the studies analyzed, and inability to use prior evidence to select the best fraud detection method(s). Additional research designed to address these gaps would be of value to researchers, policymakers, and health care practitioners who aim to select the best fraud detection methods for their specific area of practice.

An Empirical Study of Lenders’ Perception of Chinese Online Peer-to-Peer (P2P) Lending Platforms

Online peer-to-peer (P2P) lending volume has rapidly increased in China because of high financing and investment demands. However, high borrowing default and fraud rates have negatively affected Chinese online P2P lending because of its unique financial and undeveloped credit systems. They have prompted the Chinese government to issue regulations on Internet finance behavior to support online P2P lending. This research investigates online P2P lenders’ investment perceptions to understand investment intention related to online P2P lending. The research model is based on planned behavior theory. A questionnaire on attitudes collected data from voluntary participants; the data were analyzed with confirmatory factor analysis and structural equation modeling. The findings show that lenders’ attitudes, behavioral controls, and subjective norms—and those three constructs’ antecedents, except relative advantages—have a positive and significant influence on lenders’ online P2P lending intentions. <b>TOPICS:</b>Fixed income and structured finance, credit risk management, emerging markets <b>Key Findings</b> ▪ Online peer-to-peer (P2P) lending volume has rapidly increased in China because of high financing and investment demands. ▪ High borrowing default and fraud rates have negatively affected Chinese online P2P lending because of its unique financial and undeveloped credit systems and have prompted the Chinese government to issue regulations on Internet finance behavior to support online P2P lending. ▪ The results indicate that lenders’ attitudes, behavioral controls, subjective norms, and those three constructs’ antecedents, except relative advantages, have a positive and significant influence on lenders’ online P2P lending intentions.

U.S. Political Alignment and Corporate Fraud

I investigate whether U.S. political geography has an impact on convictions for corporate fraud. Prior research shows that the degree of alignment between state politicians and the President’s political party is positively correlated with measures of earnings management for firms that are headquartered in the state. Typically, a high degree of political alignment affects a firm’s information and enforcement environment due to increases in policy risk and more lenient oversight, which are conducive to earnings management. I posit that such an environment is also conducive to corporate fraud and test this hypothesis using annual data for 2003-2017. The analysis is conducted using pooled OLS and panel regressions. I find that there is a positive and statistically significant relationship between corporate fraud conviction rates by state and a state-level measure of political alignment. This finding is robust to using alternative empirical specifications and ways of measuring fraud rates and political alignment.

Comparative study on credit card fraud detection based on different support vector machines

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

Does Voting by Mail Increase Fraud? Estimating the Change in Reported Voter Fraud When States Switch to Elections By Mail

We estimate the change in the reported number of voter fraud cases when states switch to conducting elections by mail. We consider two types of states in which voting is facilitated by mail: states where a large number of voters receive ballots by mail (receive-by-mail states, RBM) and a subset of these states where registered voters are automatically sent ballots by mail (vote-by-mail states, VBM). We then compare the number of voter fraud cases in RBM (VBM) states to the number of cases in non-RBM (non-VBM) states, using two approaches standard in the social sciences. We find no evidence that voting by mail increases the risk of voter fraud overall. Between 2016 and 2019, RBM (VBM) states reported similar fraud rates to non-RBM (non-VBM) states. Moreover, we estimate Washington would have reported 73 more cases of fraud between 2011 and 2019 had it not introduced its VBM law. While our analysis of the data considers only two of many possible approaches, we argue our findings are unlikely were fraud more common when elections are held by mail.

Technology innovation in the Nigerian banking system: prospects and challenges

PurposeThe banking system in Nigeria is gradually moving away from transactions “across the counter” to the fingertips of the customers with the adoption of modern technology. However, every development comes with its “pros and cons” because as technology innovation has improved service delivery and profitability of banks in Nigeria, crimes are also at a high side. To activating the minds of bank operators about the importance of technology adoption and its shortcomings, this paper aims to examine the prospects and challenges of technology innovation in the Nigerian banking system.Design/methodology/approachSecondary data were retrieved from the annual reports of Central Bank of Nigeria (CBN) and Nigeria Deposit Insurance Corporation (NDIC) from 2013 to 2017 to know the interaction between e-banking platforms and performance of banks in Nigeria. The study administered a questionnaire to the bank customers in Lagos Island, Nigeria to understand their perception towards e-banking. This study is anchored on prospect theory to ascertain the risk orientation of the Nigerian banks regarding how they adopt technology and reasoned action theory to understand the intention of bank customers in using the opportunities of e-banking copiously.FindingsThe findings of this study reveal the migration from cheques to electronic related transactions. It further indicates a high rate of fraud committed through those channels. The analysis of primary data shows that innovation adoption, service quality, cybercrime have significant relationship with the competitiveness of banks, the intention of bank customers, and perception of customers towards online services. However, the rate of frauds does not have significant relationship with the usage of mobile banking products which further studies can critically examine.Originality/valueThis study has revealed available huge potentials in the e-banking that are yet to be used in Nigeria. However, consumer orientation needs to be worked on, because, customers still have the fear that cybercrime is mostly committed via e-banking platforms. Unlike in developed countries whereby quite good numbers of customers make use of e-banking platforms, majority of bank customers in Nigeria still prefer using manual methods and the world is already on the verge of moving into 5 G from 4 G.

Accounting Students’ Academic Fraud: Empirical Evidence from Indonesia

The high rates of academic fraud in many countries make this research important to be conducted. This research aimed at investigating the factors which influenced accounting students’ academic fraud as seen from fraud triangle theory and 220 accounting students were randomly sampled to be its samples. The data were collected using a questionnaire distributed either in person to the respondents or using a google form. The data were analyzed using multiple regression. The research results indicated that fraud triangle (pressure, opportunity, rationalization) and academic entitlement had a significant influence on accounting students’ academic fraud. This means lecturers could minimize academic fraud among their students by reducing pressure, opportunity and rationalization as well as academic entitlement

A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach

Electronic payment systems continue to seamlessly aid business transactions across the world, and credit cards have emerged as a means of making payments in E-payment systems. Fraud due to credit card usage has, however, remained a major global threat to financial institutions with several reports and statistics laying bare the extent of this challenge. Several machine learning techniques and approaches have been established to mitigate this prevailing menace in payment systems, effective amongst which are ensemble methods and cost-sensitive learning techniques. This paper proposes a framework that combines the potentials of meta-learning ensemble techniques and cost-sensitive learning paradigm for fraud detection. The approach of the proposed framework is to allow base-classifiers to fit traditionally while the cost-sensitive learning is incorporated in the ensemble learning process to fit the cost-sensitive meta-classifier without having to enforce cost-sensitive learning on each of the base-classifiers. The predictive accuracy of the trained cost-sensitive meta-classifier and base classifiers were evaluated using Area Under the Receiver Operating Characteristic curve (AUC). Results obtained from classifying unseen data show that the cost-sensitive ensemble classifier maintains an excellent AUC value indicating consistent performance across different fraud rates in the dataset. These results indicate that the cost-sensitive ensemble framework is efficient in producing cost-sensitive ensemble classifiers that are capable of effectively detecting fraudulent transactions in different databases of payment systems irrespective of the proportion of fraud cases as compared to the performances of ordinary ensemble classifiers.

Tax Compliance and Investment Incentives: Firm Responses to Accelerated Depreciation in China

We evaluate the effects of a Chinese accelerated depreciation policy that occurred in 2014 on firm investment. We present three findings. First, on average, the policy increased firms’ investment in eligible capital, especially the purchase of equipment and machines. Second, the policy effects are stronger for larger firms, firms with more cash, and firms with better access to finance, which tend to be less financially constrained but have better tax compliance. Third, the effect magnitude increases with imputed county tax enforcement but decreases with provincial tax fraud rate. These results shed light on the importance of improving tax compliance in making tax incentives effective.

DNA barcoding revealing seafood mislabeling in food services from Spain

Commercialization of seafood is one of the most complex food chains at international level and it is therefore one of the food commodities most likely to face challenges related to fraud and authenticity. In this sense, several studies have revealed a high percentage of fraud for species substitution both at retail and food service level worldwide including European Union. Spain is one of the European countries with the highest consumption of fisheries and aquaculture products and it is therefore a very interesting region to evaluate the impact of seafood fraud in food services. The study presented in this paper is the first large-scale attempt to study the rate of seafood fraud in food services across Spain. A total of 313 samples were collected in 204 mass caterer outlets from 15 Spanish Autonomous Communities and successfully sequenced. DNA barcoding revealed that 50 % of the food services establishments sold mislabeled seafood. Dusky grouper and tope shark showed the highest mislabelling percentages ranging from 86 to 100 %. Overall results evidence the complexity of international seafood supply chains supported by the fact that many of the species reported substituting local species, are coming from Asian, South American and South African regions.

Developing Single Nucleotide Polymorphisms for Identification of Cod Products by RAD-Seq.

Simple SummaryAs high-value fishery products, cod species are frequently faked by non-cod ones. Genetic fingerprinting is important for both certifying authenticity and traceability of fish species. In this study, we developed a method that combines DNA barcoding and restriction-site associated DNA sequencing (RAD-Seq) approach for the identification of cod products. Two sequences that contain single nucleotide polymorphism (SNP)s were identified and these SNPs can be used to distinguish three different cod species, which are Atlantic cod (Gadus morhua), Greenland turbot (Reinhardtius hippoglossoides), and Patagonian toothfish (Dissostichus eleginoides). This SNP-based method will help us to identify the products, which are sold under the name of “Xue Yu” (Cod) in China, and works in parallel with existing fish identification techniques to establish an efficient framework to detect and prevent fraud at all points of the cod commercialization.The increase in the rate of seafood fraud, particularly in the expensive fishes, forces us to verify the identity of marine products. Meanwhile, the definition of cod lacks consistency at the international level, as few standards and effective application methods are capable of accurately detecting cod species. Genetic fingerprinting is important for both certifying authenticity and traceability of fish species. In this study, we developed a method that combines DNA barcoding and the restriction-site associated DNA sequencing (RAD-Seq) approach for the identification of cod products. We first obtained 6941 high-quality single nucleotide polymorphism (SNP)s from 65.6 gigabases (Gb) of RAD-Seq raw data, and two sequences that contain SNPs were finally used to successfully identify three different cod product species, which are Atlantic cod (Gadus morhua), Greenland turbot (Reinhardtius hippoglossoides), and Patagonian toothfish (Dissostichus eleginoides). This SNP-based method will help us to identify the products, which are sold under the name of “Xue Yu” (Cod) in China, and works in parallel with existing fish identification techniques to establish an efficient framework to detect and prevent fraud at all points of the seafood supply chain.

Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection

Financial fraud is a threat which is increasing on a greater pace and has a very bad impact over the economy, collaborative institutions and administration. Credit card transactions are increasing faster because of the advancement in internet technology which leads to high dependence over internet. With the up-gradation of technology and increase in usage of credit cards, fraud rates become challenge for economy. With inclusion of new security features in credit card transactions the fraudsters are also developing new patterns or loopholes to chase the transactions. As a result of which behavior of frauds and normal transactions change constantly. Also the problem with the credit card data is that it is highly skewed which leads to inefficient prediction of fraudulent transactions. In order to achieve the better result, imbalanced or skewed data is pre-processed with the re-sampling (over-sampling or under sampling) technique for better results. The three different proportions of datasets were used in this study and random under-sampling technique was used for skewed dataset. This work uses the three machine learning algorithms namely: logistic regression, Naive Bayes and K-nearest neighbour. The performance of these algorithms is recorded with their comparative analysis. The work is implemented in python and the performance of the algorithms is measured based on accuracy, sensitivity, specificity, precision, F-measure and area under curve. On the basis these measurements logistic regression based model for prediction of fraudulent was found to be a better in comparison to other prediction models developed from Naive Bayes and K-nearest neighbour. Better results are also seen by applying under sampling techniques over the data before developing the prediction model.

Tournament Culture and Corporate Misconduct : Evidence using Machine Learning

This paper examines the type of corporate culture that leads to misconduct activities. Taking the management's tone in the 10-K report as the culture, we found that a higher internal compete culture (or tournament culture) increases the rate of misconduct activities such as restatements, earnings management, and financial fraud. A 1% change in tournament culture increases the chance of misconduct activities by 5% due to the firm's idiosyncratic risk. We externally validate culture measures using the company's Glassdoor rating, CSR score, and other cultural proxies. The results are robust regarding systematic risk, market competition, governance characteristics, CEO effects, and endogeneity effects.

Payment Card Fraud Rates in the United States Relative to Other Countries since Migrating to Chip Cards

The United States has lagged somewhat behind other countries in implementing steps to mitigate payment card fraud, such as chip card technology and personal identification numbers. Small delays in implementing fraud mitigation strategies could translate to large fraud losses relative to other countries. Although comparing fraud rates across countries can be challenging, Fumiko Hayashi examines payment card fraud rates in the United States along with three countries with the best available data?Australia, France, and the United Kingdom?and finds that the United States has the highest overall fraud rate. Even after migrating to chip card technology, the United States has a significantly higher in-person fraud rate than all three countries but a lower remote fraud rate than Australia and France. Fewer safeguards and differences in prevalent types of transactions may help explain this.

Tax Enforcement and Investment Incentives: Firm Responses to Accelerated Depreciation in China

We evaluate the effects of a Chinese accelerated depreciation policy that occurred in 2014 on firm investment. We present three findings. First, on average, the policy increased firms' investment in eligible capital, especially the purchase of equipment and machines. Second, the policy effects are stronger for larger firms, firms with more cash, and firms with better access to finance, which tend to be less financially constrained but have better tax compliance. Third, the effect magnitude increases with imputed county tax enforcement but decreases with provincial tax fraud rate. These results shed light on the importance of improving tax compliance in making tax incentives effective.

Combating insider fraud in Financial Institutions/impact

The fear of fraud is constant. Unfortunately, now more than ever before, fraud is being committed by employees on the inside, the very people who are supposed to be supporting and protecting an organization. Even though the financial industry is one of the most regulated, financial institution are still getting with the highest rate of internal fraud. Insider threat has always existed within each Financial Institution. In the recent years, insider threat has become a more prominent issue because of the emerging trends in the workplace. This change to a more flexible and productive workplace environment allows employees to easily gain access to an organization’s critical and sensitive information. While the risk of insider threat has certainly increased, Financial Institution have not deployed enough controls to mitigate this risk either because they believe that the frequency of such threat is very low or because they feel powerless to do so.&#x0D; This paper tends to employ techniques that would abate the spate of Insider fraud and cybercrime on customer transactions and insider processing which is in full compliance with most regulatory mandate of Countries Government bank.

Credit Card Fraud Detection using Machine Learning Algorithms

Abstract Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy [1], to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers [3],[5],[6],[8] are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift [1]. In this paper, we worked with European credit card fraud dataset.

Detection of fraud by addition cow s milk in cheese buffalo and its connection with seasonality

The seasons influence the production of buffalos’ milk. Because of this, the producers may produce a mixture of buffalo and bovine milk during cheese production in periods of low production. Therefore, the present work aimed to investigate fraud in buffalo cheese and the relationship between seasonality and different physicochemical properties of buffalo cheeses produced and marketed in eastern Amazonia. We obtained commercial samples of buffalo cheese during two Amazonian climatic periods from commercial points of Marajo-Para, Brazil. After collection, there were lipid, protein, ash, and humidity analyses. Determination of carbohydrates and energy values was also performed for the nutritional characterization of samples, as well as for mPCR analysis to detect buffalo and/or bovine DNA. DNA extraction protocol of the samples was standardized and two pairs were used for the mPCR reaction, amplifying fragments of approximately 220 bp for Bubalus bubalis DNA and 346 bp fragments for Bos taurus DNA. Among the samples acquired in the rainy season, we observed that 33% were inadequately labeled, indicating fraud from cow’s milk incorporation and fraud from substitution of raw material. From the nine samples obtained in the dry season, all the samples showed cow’s milk incorporation fraud. The highest fraud rate coincided with the period of low milk production from buffalo and there was a difference in composition between fraudulent and non-fraudulent cheeses. Therefore, seasonality influences increase in cattle milk for the production of buffalo cheese, and this adulteration may decrease the nutritional content of the product.

The Initial Effects of EMV Migration on Chargebacks in the United States

To reduce counterfeit fraud in the card-present environment, the United States started migrating to EMV chip technology in the mid-2010s. Since October 2015, merchants have been liable for counterfeit fraud committed using EMV cards if the merchants had not adopted EMV chip-readable terminals. In particular, merchants are held liable through chargebacks. {{p}} This study examines the initial effects of the EMV liability shift on fraud chargeback and merchant loss rates using data from merchant processors and PIN debit networks. Combined with gross fraud rates?overall fraud rates regardless of who incurs fraud losses?estimated in other studies, the results of our study suggest that merchants have faced a significantly higher share of fraud losses since the shift; however, this spike will decline if merchants continue to adopt EMV. Merchant fraud loss rates for signature-based transactions in the card-present channel increased sixfold, but the rates significantly vary between magnetic stripe and chip-to-chip transactions. While merchant fraud loss rates for magnetic stripe transactions are over 9 basis points in value for all merchants combined and vary across merchant categories, the rates for chip-to-chip transactions are very low, around 0.02 basis points, across all merchant categories. Because the gross fraud rates for magnetic-stripe transactions did not increase after the liability shift, our results suggest that the higher merchant fraud loss rates for magnetic-stripe transactions are mainly due to the liability shift. {{p}} Compared with signature-based transactions, fraud chargeback rates for PIN debit transactions in the card-present channel are much lower. Our results suggest that both EMV and PIN are effective in reducing merchant fraud loss rates. However, we need detailed gross fraud rates to examine how effective EMV and PIN are in reducing fraud more generally in the card-present channel. {{p}} Our results for card-not-present fraud chargeback and merchant loss rates are mixed. Both rates increased for some merchant categories, but the rates for all merchants combined actually decreased in our data. This decline is likely due to the underrepresentation of signature-based CNP transactions in our data. The gross fraud rates for card-not-present transactions increased over the same period, and merchants are generally liable for card-not-present fraud.